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MSc Sociology and Data Science

  • DeadlineStudy Details: 1 year - 5 years

Masters Degree Description

The Sociology and Data Science MSc combines advanced sociological training with rigorous data science skills to equip you with the tools to analyse and interpret complex social data. Through a curriculum that integrates sociological theory with cutting-edge data science techniques, you will explore pressing social issues using computational methods, quantitative analysis, and statistical software. Topics that may be covered include social inequalities, digital and global transformations, social mobility, and the role of data science in driving evidence-based policy.

Entry Requirements

A minimum of an upper second-class Bachelors' degree from a UK university or an overseas qualification of an equivalent standard in sociology or a related social science subject. If you do not meet these requirements individuals with relevant work experience, or with A level sociology and an undergraduate degree in another subject who show a clear desire to change discipline may be considered.

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Fees

For fees and funding options, please visit website to find out more

Programme Funding

UCL offers a range of financial awards aimed at assisting both prospective and current students with their studies.

Student Destinations

This is a new degree so there are no alumni yet.

Module Details

Compulsory modules

  • Global Sociology
  • Principles of Sociological Explanation
  • Social Change in Contemporary Society
  • Advanced Social Theory
  • Dissertation
  • Advanced Social Data Science
  • Foundations of Social Data Science
  • Advanced Computational Techniques for Data Science
  • Understanding Data for the Social Sciences
  • Impact Evaluation Methods
  • Longitudinal Data and Analysis
  • Data Science using International Data

Optional modules

  • Advanced Social Data Science
  • Foundations of Social Data Science
  • Advanced Computational Techniques for Data Science
  • Understanding Data for the Social Sciences
  • Impact Evaluation Methods
  • Longitudinal Data and Analysis
  • Data Science using International Data

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